ASSIMILATION OF PASSIVE MICROWAVE BRIGHTNESS TEMPERATURES FOR SNOW WATER EQUIVALENT ESTIMATION USING THE NASA CATCHMENT LAND SURFACE MODEL AND MACHINE LEARNING ALGORITHMS IN NORTH AMERICA

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dc.contributor.advisorForman, Barton A.en_US
dc.contributor.authorXue, Yuanen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2017-09-13T05:36:56Z
dc.date.available2017-09-13T05:36:56Z
dc.date.issued2017en_US
dc.description.abstractSnow is a critical component in the global energy and hydrologic cycle. It is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. To accurately estimate the depth of snow and mass of water within a snow pack across regional or continental scales is a challenge, especially in the presence of dense vegetations since direct quantification of SWE is complicated by spatial and temporal variability. To overcome some of the limitations encountered by traditional SWE retrieval algorithms or radiative transfer-based snow emission models, this study explores the use of a well-trained support vector machine to merge an advanced land surface model within a variant of radiance emission (i.e., brightness temperature) assimilation experiments. In general, modest improvements in snow depth, and SWE predictability were witnessed as a result of the assimilation procedure over snow-covered terrain in North America when compared against available snow products as well as ground-based observations. These preliminary findings are encouraging and suggest the potential for global-scale snow estimation via the proposed assimilation procedure.en_US
dc.identifierhttps://doi.org/10.13016/M2SQ8QJ1D
dc.identifier.urihttp://hdl.handle.net/1903/19813
dc.language.isoenen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pqcontrolledWater resources managementen_US
dc.subject.pquncontrolledatmosphereen_US
dc.subject.pquncontrolleddata assimilationen_US
dc.subject.pquncontrolledmachine learningen_US
dc.subject.pquncontrolledpassive microwaveen_US
dc.subject.pquncontrolledsnow water equivalenten_US
dc.subject.pquncontrolledvegetationen_US
dc.titleASSIMILATION OF PASSIVE MICROWAVE BRIGHTNESS TEMPERATURES FOR SNOW WATER EQUIVALENT ESTIMATION USING THE NASA CATCHMENT LAND SURFACE MODEL AND MACHINE LEARNING ALGORITHMS IN NORTH AMERICAen_US
dc.typeDissertationen_US

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